Sine Cosine Algorithm (SCA) is a newly proposed competent population-based metaheuristic, which has gained a multi-disciplinary interest in solving optimization problems. Like other metaheuristics, the performance of SCA is sensitive to the settings of its parameters and one such parameter is Population Size (PS). There is no one population size that fits all; problem uniqueness requires a matching strategy of parameter selection and adaptation. However, in standard SCA and its variants, PS is treated as a usercontrolled parameter and no study has explored the effect of PS adaptation on SCA's performance. To fill this research gap, this study investigates and compares the impact of promising strategies for setting and controlling population size, from other metaheuristics, on the performance of the standard SCA and five of its variants in terms of fitness, run-time, and convergence characteristics. Finding the best PS setting for a metaheuristic is a challenging problem since it depends on both the nature of the algorithm used and the problem being solved. Leading PS adaptation techniques considered in this study are linear staircase reduction, iterative halving, reinitialization and incrementation, pulse wave, population diversity, and three parent crossover strategies. A classic set of 23 well-known benchmark functions has been utilized for a fixed number of evaluations to assess the impact of each PS adaptation strategy on the performance of SCA and its variants. Also, non-parametric Wilcoxon's rank sum test is performed to provide a comprehensive view of various PS adaptation strategies' performance with respect to each other in terms of fitness and run-time. Simulation results reveal that proper selection of PS adaptation strategy can further enhance the exploration and exploitation capabilities of SCA and its variants.

Fuzzy logic controllers are readily customizable in natural language terms, and can effectively deal with non-linearities and uncertainties in control systems. This paper presents an intelligent and automated fuzzy control procedure for the refrigerant charging of refrigerators. The elements that affect the experimental charging and the optimization of the performance of refrigerators are fuzzified and used in an inference model. The objective is to represent the intelligent behavior of a human tester, and ultimately make the developed model available for the use in an automated data acquisition, monitoring, and decision-making system. The proposed system is capable of determining the needed amount of refrigerant in the shortest possible time. The system automates the refrigerant charging and performance testing of parallel units. The system is built using data acquisition systems from National Instruments and programmed under LabVIEW. The developed fuzzy models, and their testing results, are evaluated according to their compatibility with the principles that govern the intelligent behavior of human experts when performing the refrigerant charging process. In addition, comparisons of the fuzzy models with classical inference models are presented. The obtained results confirm that the proposed fuzzy controllers outperform traditional crisp controllers and provide major test time and energy savings. The paper includes thorough discussions, analysis, and evaluation.